Leveraging the naturally occurring spotted pigmentation of Hampshire swine to assess the impact of skin pigmentation on pulse oximeters and other light-based medical devices (Conference Presentation)
Bibliographic record
Abstract
Light-based devices, like pulse oximeters and imaging systems, detect light after it interacts with skin. Melanin’s strong optical absorption may cause disparate device performance between lightly and darkly pigmented people. It's critical that devices work equitably across the full spectrum of pigmentation, but there in an unmet need for a means of device testing where pigment varies while other physiologic variables are constant. We address this need by validating devices in Hampshire swine that have large patches of pigmented and nonpigmented skin (PS, NPS). Placing duplicate devices on PS and NPS patches in the same animal during device validation studies directly compares the impact of pigmentation on device performance while controlling for other physiologic factors. This model is a novel approach to study how pigment impacts light-based medical modalities, which is critical to ensuring equitable device performance across the full spectrum of skin pigmentation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".